import sco

# Build the Benson17 version of the model:
mdl = sco.build_model('benson17')

# Ask it what its afferent parameters are:
mdl.afferents
# => ('contrast_constants_by_label', 'aperture_radius', 'create_directories',
# =>  'max_eccentricity', 'measurements_filename', 'modality', 'subject',
# =>  'divisive_exponents_by_label', 'pixels_per_degree',
# =>  'pRF_sigma_slopes_by_label', 'output_suffix',
# =>  'divisive_normalization_schema', 'pRF_n_radii',
# =>  'compressive_constants_by_label', 'output_directory', 'background',
# =>  'normalized_pixels_per_degree', 'gabor_orientations',
# =>  'aperture_edge_width', 'stimulus', 'output_prefix', 'import_filter',
# =>  'cpd_sensitivity_function', 'saturation_constants_by_label', 'gamma')

# Ask it which parameters have default values (and what they are):
# (note that a pmap is a kind of persistent dict object)
mdl.defaults
# => pmap({'contrast_constants_by_label': 'sco.impl.benson17.contrast_constants_by_label_Kay2013',
# =>       'normalized_pixels_per_degree': 12, 'gabor_orientations': 8,
# =>       'pRF_n_radii': 3.0, 'aperture_radius': None, 'output_prefix': '',
# =>       'gamma_correction_function': None, 'aperture_edge_width': None,
# =>       'background': 0.5, 'modality': 'volume', 'gamma': None
# =>       'compressive_constants_by_label': 'sco.impl.benson17.compressive_constants_by_label_Kay2013',
# =>       'divisive_exponents_by_label': 'sco.impl.benson17.divisive_exponents_by_label_Kay2013',
# =>       'pRF_sigma_slopes_by_label': 'sco.impl.benson17.pRF_sigma_slopes_by_label_Kay2013',
# =>       'divisive_normalization_schema': 'Heeger1992',
# =>       'cpd_sensitivity_function': 'sco.impl.benson17.cpd_sensitivity',
# =>       'saturation_constants_by_label': 'sco.impl.benson17.saturation_constants_by_label_Kay2013',
# =>       'max_eccentricity': 12, 'output_suffix': '', 'import_filter': None,
# =>       'create_directories': False})

# Ask it about one of the parameters
print mdl.afferent_docs['subject']
# => (import_subject) subject: Must be one of (a) the name of a FreeSurfer subject found on the subject path,
# =>         (b) a path to a FreeSurfer subject directory, or (c) a neuropythy FreeSurfer subject
# =>         object.

# Ask it what outputs it produces:
mdl.efferents.keys()
# => pvector(['prediction_analysis', 'pRF_SOC', 'freesurfer_subject',
# =>          'stimulus_ordering', 'contrast_constants', 'contrast_energies',
# =>          'exported_report_filenames', 'coordinates', 'measurements',
# =>          'pRFs', 'divisive_normalization_parameters', 'hemispheres',
# =>          'pRF_sigma_slopes', 'compressive_constants',
# =>          'measurement_hemispheres', 'pRF_radii',
# =>          'exported_analysis_filenames', 'measurement_per_prediction',
# =>          'pRF_sigmas', 'cortex_coordinates', 'cortex_indices',
# =>          'polar_angles', 'prediction', 'prediction_per_measurement',
# =>          'cpd_sensitivities', 'pixel_centers', 'image_names',
# =>          'divisive_normalization_function', 'contrast_filter',
# =>          'stimulus_map', 'measurement_indices', 'image_array',
# =>          'exported_files', 'exported_predictions_filenames',
# =>          'benson17_default_options_used', 'gamma_correction_function',
# =>          'eccentricities', 'labels', 'corresponding_indices',
# =>          'pRF_centers', 'cortex_affine', 'prediction_analysis_labels',
# =>          'measurement_coordinates'])

# Ask for help about of of the ouptuts:
print mdl.efferent_docs['prediction']
# => (compressive_nonlinearity) prediction: Will be the final predictions of %BOLD-change for each pRF examined, up to gain.
# =>         The data will be stored in an (n x m) matrix where n is the number of pRFs (see labels,
# =>         hemispheres, cortex_indices) and m is the number of images.

# Results may be generated by providing the model with parameters; parameters
# may be provided as a dict of key-value pairs (optional) followed by any number
# of keyword arguments:
results = mdl({'subject': 'test-sub-01', 'max_eccentricity': 10},
              pixels_per_degree=6, stimulus=stimulus_file_list))

# results is now a dict-like object; the keys are the efferent (output) data listed above;
# the values do not get calculated until you request them, so while the above line that
# begins with "results = mdl(..." return immediately, the following line will take some
# time to run:
results['prediction'].shape #should be vox-by-imgs
# => (17551, 120)

# The following like will force all exported files in the standard 'benson17' model to be
# exported:
results['exported_files']
# => <list of filenames for exported prediction, images, reports, etc.>